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Multifile probabilistic record linkage for drug overdose surveillance and public health action

用于药物过量监测和公共卫生行动的多文件概率记录链接

基本信息

批准号：
10039949
负责人：
Julia Elizabeth Hood
金额：
$ 18.71万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10039949
关键词：
Address Adoption Bayesian Method Big Data Methods Books Characteristics Complex Computer software County Data Data Collection Data Sources Databases Date of birth Death Certificates Detection Development Emergency medical service Ensure Epidemiologist Evaluation Government Government Agencies Incidence Individual Information Systems Internet Interview Jail Light Link Measures Methodology Names Output Overdose Performance Pharmaceutical Preparations Pilot Projects Prevention program Prevention strategy Process Public Health Records Techniques Technology Testing Training administrative database base data integration flexibility graphical user interface improved information model multiple data sources new technology open source operation overdose prevention overdose risk referral services simulation treatment services usability

项目摘要

Record linkage refers to the process of integrating data by identifying unique individuals within and across data sources. In administrative databases, it is common to have a limited amount of the individuals' partial identifiers, such as names or dates of birth, which together with typographical errors and missing data, makes the record linkage task difficult and prone to errors. Probabilistic record linkage approaches have been shown to have superior performance when compared with ruled-based deterministic techniques, as probabilistic approaches adapt better to different and increased levels of error in the datafiles. Existing probabilistic approaches are nevertheless subject to different limitations. In practice, it is common to encounter data integration scenarios where multiple data sources need to be simultaneously merged and deduplicated using imperfect information such as names, dates or addresses. These scenarios go beyond the specifications for which commonly used record linkage and deduplication methodologies have been developed. We therefore propose to extend the currently-available best-performing record linkage methodologies to simultaneously integrate multiple datafiles and detect duplicated records within them. We will develop this methodology, with an associated software and graphical user interface, in partnership with Public Health – Seattle & King County to ensure that these are responsive to real world needs and challenges. We will also conduct a pilot study implementing the techniques on King County administrative data systems used for overdose surveillance and evaluation of overdose prevention programs.

记录链接是指通过识别数据内和数据间的唯一个体来整合数据的过程源在管理数据库中，通常具有有限数量的个人部分姓名或出生日期等标识符，再加上印刷错误和数据缺失，记录联动任务困难且容易出错。概率记录联系方法已被证明与基于规则的确定性技术相比，具有上级性能，这些方法更好地适应不同的和增加的误差水平。现有概率然而，各种方法受到不同的限制。在实践中，经常会遇到数据需要同时合并多个数据源并使用不完整的信息，如姓名、日期或地址。这些场景超出了已经开发了哪些常用的记录链接和去重方法。因此我们建议将现有的最佳记录链接方法扩展到同时集成多个文件并检测其中重复记录。我们将开发这种方法，一个相关的软件和图形用户界面，与公共卫生-西雅图和金郡合作以确保这些机制能够满足真实的世界需求和挑战。我们亦会进行试验研究在金郡行政数据系统上实施用于过量监测的技术，过量预防计划的评估。